GTES: An Optimized Game-Theoretic Demand-Side Management Scheme for Smart Grid

Fadlullah, Zubair Md.; Duong, Minh Quan; Kato, Nei; Stojmenović, Ivan

doi:10.1109/jsyst.2013.2260934

Cited by 169 publications

(119 citation statements)

References 16 publications

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“…The work [18] investigated how energy consumption may be optimized by taking into consideration the interaction between both parties. The energy price model is a function of total energy consumption.…”

Section: B Similar Workmentioning

confidence: 99%

“…Each user then optimizes his own schedule and reports it to the supplier, which in turn updates its energy price parameter before pulling the next consumers. This interaction between the power company and its consumers is modelled through a two-step centralized game, based on which the work [18] proposed the GameTheoretic Energy Schedule (GTES) method. The objective of the GTES method is to reduce the peak to average power ratio by optimizing the users energy schedules.…”

Section: B Similar Workmentioning

confidence: 99%

See 1 more Smart Citation

The Fog Computing Paradigm: Scenarios and Security Issues

Stojmenović¹,

Wen²

2014

Annals of Computer Science and Information Systems

Self Cite

775

438

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Abstract-Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. In this article, we elaborate the motivation and advantages of Fog computing, and analyse its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks. We discuss the state-of-the-art of Fog computing and similar work under the same umbrella. Security and privacy issues are further disclosed according to current Fog computing paradigm. As an example, we study a typical attack, man-in-the-middle attack, for the discussion of security in Fog computing. We investigate the stealthy features of this attack by examining its CPU and memory consumption on Fog device.

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Section: B Similar Workmentioning

confidence: 99%

Section: B Similar Workmentioning

confidence: 99%

The Fog Computing Paradigm: Scenarios and Security Issues

Stojmenović¹,

Wen²

2014

Annals of Computer Science and Information Systems

Self Cite

775

438

View full text Add to dashboard Cite

show abstract

“…where ψ (x,t) denotes the ON/OFF status of appliance x at time slot t. The appliance ON/OFF mechanism manages the scheduling process more realistically as compared to simple time-based models discussed in [30,31]. For example, if a user wants to stop any appliance during high price hours 08:00 → 10:00, the ON/OFF mechanism is easy to model.…”

Section: The Proposed Scheduling Algorithmmentioning

confidence: 99%

“…The energy consumption of h 2 is relatively less due to the activity recognition system. In h 1 , the working of the HVAC is controlled on the basis of inside/outside room temperature differences given in [3,[29][30][31][32][33]. In h 2 , human presence along with outside/inside temperature difference is also incorporated in controlling the temperature set points of the HVAC.…”

Section: Impact Of Seasons On the Energy Optimizationmentioning

confidence: 99%

Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing

et al. 2016

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Abstract:In this paper, we present an energy optimization technique to schedule three types of household appliances (user dependent, interactive schedulable and unschedulable) in response to the dynamic behaviours of customers, electricity prices and weather conditions. Our optimization technique schedules household appliances in real time to optimally control their energy consumption, such that the electricity bills of end users are reduced while not compromising on user comfort. More specifically, we use the binary multiple knapsack problem formulation technique to design an objective function, which is solved via the constraint optimization technique. Simulation results show that average aggregated energy savings with and without considering the human presence control system are 11.77% and 5.91%, respectively.

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“…Email address: bing.zhu@ntu.edu.sg (Bing Zhu) et al, 2015), or between the power company and users (Fadlullah et al, 2014). Pay-off functions and strategies are usually defined such that existence of Nash Equilibrium (NE) can be proved.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary game theoretic demand-side management and control for a class of networked smart grid

Zhu

Xia

2016

Automatica

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In this paper, a new demand-side management problem of networked smart grid is formulated and solved based on evolutionary game theory. The objective is to minimize the overall cost of the smart grid, where individual communities can switch between grid power and local power according to strategies of their neighbors. The distinctive feature of the proposed formulation is that, a small portion of the communities are cooperative, while others pursue their own benefits. This formulation can be categorized as control networked evolutionary game, which can be solved systematically by using semi-tensor product. A new binary optimal control algorithm is applied to optimize transient performances of the networked evolutionary game.

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GTES: An Optimized Game-Theoretic Demand-Side Management Scheme for Smart Grid

Cited by 169 publications

References 16 publications

The Fog Computing Paradigm: Scenarios and Security Issues

The Fog Computing Paradigm: Scenarios and Security Issues

Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing

Evolutionary game theoretic demand-side management and control for a class of networked smart grid

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